Selective peripheral nerve recordings from nerve cuff electrodes using convolutional neural networks

Recording and stimulating from the peripheral nervous system are becoming important components in a new generation of bioelectronics systems. Although neurostimulation has seen a history of successful chronic applications in humans, peripheral nerve recording in humans chronically remains a challenge. Multi-contact nerve cuff electrode configurations have the potential to improve recording selectivity. We introduce the idea of using a convolutional neural network (CNN) to associate recordings of individual naturally evoked compound action potentials (CAPs) with neural pathways of interest, by exploiting the spatiotemporal patterns in multi-contact nerve cuff recordings.

Nine Long-Evan rats were implanted with a 56-channel nerve cuff electrode on the sciatic nerve and afferent activity was selectively evoked in different fascicles (tibial, peroneal, sural) using mechanical stimuli. A recurrent neural network was then used to predict joint angles based on the predicted firing patterns from the CNN. Performance was measured based on the classification accuracy, F ₁-score and the ability to track the ankle joint angle.

Classification accuracy and F ₁-score of the best CNN configuration were [Formula: see text] and 0.747  ±  0.114, respectively. The mean Pearson correlation coefficient between the manually measured ankle angle and the angle predicted from the estimated firing rate was [Formula: see text] Significance. The proposed method demonstrates that CAP-based classification can be achieved with high accuracy and can be used to track a physiological meaningful measure (e.g. joint angle). These results provide a promising direction for realizing more effective and intuitive neuroprosthetic systems.